At a conceptual level I’m completely on board. At a practical level I fear a disaster. Right now you at least need to find a word which you can claim to be analyzing and that fact encourages a certain degree of contact and disagreement even if a hard subject like philosophy should really have 5 specific rebuttal papers (the kind journals won’t publish) for each positive proposal rather than the reverse as they do now.
The problem with conceptual engineering for philosophy is that philosophers aren’t really going to start going out and doing tough empirical work the way a UI designer might. All they are going to do is basically assert that their concept are useful/good and the underlying sociology of philosophy means it’s seen as bad form to mercilessly come after them insisting that: no that’s a stupid and useless concept. Disagreements over the adequacy of a conceptual analysis or the coherence of a certain view are considered acceptable to push to a degree (not enough imo) but going after someone overtly (rather than via rumor) because their work isn’t sufficiently interesting is a big no no. So I fear the end result would be to turn philosophy into a thousand little islands each just gazing at their own navel with no one willing to argue that your concepts aren’t useful enough.
This is an interesting direction to explore but as is I don’t have any idea what you mean by understand the go bot and I fear figuring that out would itself require answering more than you want to ask.
For instance, what if I just memorize the source code. I can slowly apply each step on paper and as the adversarial training process has no training data or human expert input if I know the rules of go I can, Chinese room style, fully replicate the best go bot using my knowledge given enough time.
But if that doesn’t count and you don’t just mean be better than them at go then you must have in mind that I’d somehow have the same ‘insights’ as the program. But now to state the challenge we need a precise (mathematical) definition that specifies the insights contained in a trained ML model which means we’ve already solved the problem.